#!/usr/bin/env python
""" EfficientDet Training Script

This script was started from an early version of the PyTorch ImageNet example
(https://github.com/pytorch/examples/tree/master/imagenet)

NVIDIA CUDA specific speedups adopted from NVIDIA Apex examples
(https://github.com/NVIDIA/apex/tree/master/examples/imagenet)

Hacked together by Ross Wightman (https://github.com/rwightman)
"""
import argparse
import time
import yaml
from datetime import datetime

import torch
import torchvision.utils
try:
    from apex import amp
    from apex.parallel import DistributedDataParallel as DDP
    from apex.parallel import convert_syncbn_model
    has_apex = True
except ImportError:
    from torch.nn.parallel import DistributedDataParallel as DDP
    has_apex = False

from effdet import create_model, COCOEvaluator, unwrap_bench
from data import create_loader, CocoDetection

from timm.models import resume_checkpoint, load_checkpoint
from timm.utils import *
from timm.optim import create_optimizer
from timm.scheduler import create_scheduler

torch.backends.cudnn.benchmark = True


# The first arg parser parses out only the --config argument, this argument is used to
# load a yaml file containing key-values that override the defaults for the main parser below
config_parser = parser = argparse.ArgumentParser(description='Training Config', add_help=False)
parser.add_argument('-c', '--config', default='', type=str, metavar='FILE',
                    help='YAML config file specifying default arguments')


parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
# Dataset / Model parameters
parser.add_argument('data', metavar='DIR',
                    help='path to dataset')
parser.add_argument('--model', default='tf_efficientdet_d1', type=str, metavar='MODEL',
                    help='Name of model to train (default: "countception"')
parser.add_argument('--redundant-bias', action='store_true', default=False,
                    help='include redundant bias layers if True, need True to match official TF weights')
parser.add_argument('--pretrained', action='store_true', default=False,
                    help='Start with pretrained version of specified network (if avail)')
parser.add_argument('--no-pretrained-backbone', action='store_true', default=False,
                    help='Do not start with pretrained backbone weights, fully random.')
parser.add_argument('--initial-checkpoint', default='', type=str, metavar='PATH',
                    help='Initialize model from this checkpoint (default: none)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='Resume full model and optimizer state from checkpoint (default: none)')
parser.add_argument('--no-resume-opt', action='store_true', default=False,
                    help='prevent resume of optimizer state when resuming model')
parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
                    help='Override mean pixel value of dataset')
parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
                    help='Override std deviation of of dataset')
parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
                    help='Image resize interpolation type (overrides model)')
parser.add_argument('--fill-color', default='0', type=str, metavar='NAME',
                    help='Image augmentation fill (background) color ("mean" or int)')
parser.add_argument('-b', '--batch-size', type=int, default=32, metavar='N',
                    help='input batch size for training (default: 32)')
parser.add_argument('-vb', '--validation-batch-size-multiplier', type=int, default=1, metavar='N',
                    help='ratio of validation batch size to training batch size (default: 1)')
parser.add_argument('--drop', type=float, default=0.0, metavar='PCT',
                    help='Dropout rate (default: 0.)')
parser.add_argument('--drop-connect', type=float, default=None, metavar='PCT',
                    help='Drop connect rate, DEPRECATED, use drop-path (default: None)')
parser.add_argument('--drop-path', type=float, default=None, metavar='PCT',
                    help='Drop path rate (default: None)')
parser.add_argument('--drop-block', type=float, default=None, metavar='PCT',
                    help='Drop block rate (default: None)')
parser.add_argument('--clip-grad', type=float, default=10.0, metavar='NORM',
                    help='Clip gradient norm (default: 10.0)')

# Optimizer parameters
parser.add_argument('--opt', default='momentum', type=str, metavar='OPTIMIZER',
                    help='Optimizer (default: "momentum"')
parser.add_argument('--opt-eps', default=1e-3, type=float, metavar='EPSILON',
                    help='Optimizer Epsilon (default: 1e-8)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
                    help='SGD momentum (default: 0.9)')
parser.add_argument('--weight-decay', type=float, default=4e-5,
                    help='weight decay (default: 0.00004)')

# Learning rate schedule parameters
parser.add_argument('--sched', default='cosine', type=str, metavar='SCHEDULER',
                    help='LR scheduler (default: "step"')
parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
                    help='learning rate (default: 0.01)')
parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct',
                    help='learning rate noise on/off epoch percentages')
parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT',
                    help='learning rate noise limit percent (default: 0.67)')
parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
                    help='learning rate noise std-dev (default: 1.0)')
parser.add_argument('--warmup-lr', type=float, default=0.0001, metavar='LR',
                    help='warmup learning rate (default: 0.0001)')
parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR',
                    help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
parser.add_argument('--epochs', type=int, default=300, metavar='N',
                    help='number of epochs to train (default: 2)')
parser.add_argument('--start-epoch', default=None, type=int, metavar='N',
                    help='manual epoch number (useful on restarts)')
parser.add_argument('--decay-epochs', type=float, default=30, metavar='N',
                    help='epoch interval to decay LR')
parser.add_argument('--warmup-epochs', type=int, default=5, metavar='N',
                    help='epochs to warmup LR, if scheduler supports')
parser.add_argument('--cooldown-epochs', type=int, default=10, metavar='N',
                    help='epochs to cooldown LR at min_lr, after cyclic schedule ends')
parser.add_argument('--patience-epochs', type=int, default=10, metavar='N',
                    help='patience epochs for Plateau LR scheduler (default: 10')
parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE',
                    help='LR decay rate (default: 0.1)')

# Augmentation parameters
parser.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT',
                    help='Color jitter factor (default: 0.4)')
parser.add_argument('--aa', type=str, default=None, metavar='NAME',
                    help='Use AutoAugment policy. "v0" or "original". (default: None)'),
parser.add_argument('--reprob', type=float, default=0., metavar='PCT',
                    help='Random erase prob (default: 0.)')
parser.add_argument('--remode', type=str, default='const',
                    help='Random erase mode (default: "const")')
parser.add_argument('--recount', type=int, default=1,
                    help='Random erase count (default: 1)')
parser.add_argument('--resplit', action='store_true', default=False,
                    help='Do not random erase first (clean) augmentation split')
parser.add_argument('--mixup', type=float, default=0.0,
                    help='mixup alpha, mixup enabled if > 0. (default: 0.)')
parser.add_argument('--mixup-off-epoch', default=0, type=int, metavar='N',
                    help='turn off mixup after this epoch, disabled if 0 (default: 0)')
parser.add_argument('--smoothing', type=float, default=0.1,
                    help='label smoothing (default: 0.1)')
parser.add_argument('--train-interpolation', type=str, default='random',
                    help='Training interpolation (random, bilinear, bicubic default: "random")')
parser.add_argument('--sync-bn', action='store_true',
                    help='Enable NVIDIA Apex or Torch synchronized BatchNorm.')
parser.add_argument('--dist-bn', type=str, default='',
                    help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")')

# Model Exponential Moving Average
parser.add_argument('--model-ema', action='store_true', default=False,
                    help='Enable tracking moving average of model weights')
parser.add_argument('--model-ema-decay', type=float, default=0.9998,
                    help='decay factor for model weights moving average (default: 0.9998)')

# Misc
parser.add_argument('--seed', type=int, default=42, metavar='S',
                    help='random seed (default: 42)')
parser.add_argument('--log-interval', type=int, default=50, metavar='N',
                    help='how many batches to wait before logging training status')
parser.add_argument('--recovery-interval', type=int, default=0, metavar='N',
                    help='how many batches to wait before writing recovery checkpoint')
parser.add_argument('-j', '--workers', type=int, default=4, metavar='N',
                    help='how many training processes to use (default: 1)')
parser.add_argument('--save-images', action='store_true', default=False,
                    help='save images of input bathes every log interval for debugging')
parser.add_argument('--amp', action='store_true', default=False,
                    help='use NVIDIA amp for mixed precision training')
parser.add_argument('--pin-mem', action='store_true', default=False,
                    help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
parser.add_argument('--no-prefetcher', action='store_true', default=False,
                    help='disable fast prefetcher')
parser.add_argument('--output', default='', type=str, metavar='PATH',
                    help='path to output folder (default: none, current dir)')
parser.add_argument('--eval-metric', default='map', type=str, metavar='EVAL_METRIC',
                    help='Best metric (default: "map"')
parser.add_argument('--tta', type=int, default=0, metavar='N',
                    help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)')
parser.add_argument("--local_rank", default=0, type=int)


def _parse_args():
    # Do we have a config file to parse?
    args_config, remaining = config_parser.parse_known_args()
    if args_config.config:
        with open(args_config.config, 'r') as f:
            cfg = yaml.safe_load(f)
            parser.set_defaults(**cfg)

    # The main arg parser parses the rest of the args, the usual
    # defaults will have been overridden if config file specified.
    args = parser.parse_args(remaining)

    # Cache the args as a text string to save them in the output dir later
    args_text = yaml.safe_dump(args.__dict__, default_flow_style=False)
    return args, args_text


def main():
    setup_default_logging()
    args, args_text = _parse_args()

    args.pretrained_backbone = not args.no_pretrained_backbone
    args.prefetcher = not args.no_prefetcher
    args.distributed = False
    if 'WORLD_SIZE' in os.environ:
        args.distributed = int(os.environ['WORLD_SIZE']) > 1
    args.device = 'cuda:0'
    args.world_size = 1
    args.rank = 0  # global rank
    if args.distributed:
        args.device = 'cuda:%d' % args.local_rank
        torch.cuda.set_device(args.local_rank)
        torch.distributed.init_process_group(backend='nccl', init_method='env://')
        args.world_size = torch.distributed.get_world_size()
        args.rank = torch.distributed.get_rank()
    assert args.rank >= 0

    if args.distributed:
        logging.info('Training in distributed mode with multiple processes, 1 GPU per process. Process %d, total %d.'
                     % (args.rank, args.world_size))
    else:
        logging.info('Training with a single process on 1 GPU.')

    torch.manual_seed(args.seed + args.rank)

    model = create_model(
        args.model,
        bench_task='train',
        pretrained=args.pretrained,
        pretrained_backbone=args.pretrained_backbone,
        redundant_bias=args.redundant_bias,
        checkpoint_path=args.initial_checkpoint,
    )
    # FIXME decide which args to keep and overlay on config / pass to backbone
    #     num_classes=args.num_classes,
    #     drop_rate=args.drop,
    #     drop_path_rate=args.drop_path,
    #     drop_block_rate=args.drop_block,
    input_size = model.config.image_size

    if args.local_rank == 0:
        logging.info('Model %s created, param count: %d' %
                     (args.model, sum([m.numel() for m in model.parameters()])))

    model.cuda()
    optimizer = create_optimizer(args, model)
    use_amp = False
    if has_apex and args.amp:
        model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
        use_amp = True
    if args.local_rank == 0:
        logging.info('NVIDIA APEX {}. AMP {}.'.format(
            'installed' if has_apex else 'not installed', 'on' if use_amp else 'off'))

    # optionally resume from a checkpoint
    resume_state = {}
    resume_epoch = None
    if args.resume:
        resume_state, resume_epoch = resume_checkpoint(unwrap_bench(model), args.resume)
    if resume_state and not args.no_resume_opt:
        if 'optimizer' in resume_state:
            if args.local_rank == 0:
                logging.info('Restoring Optimizer state from checkpoint')
            optimizer.load_state_dict(resume_state['optimizer'])
        if use_amp and 'amp' in resume_state and 'load_state_dict' in amp.__dict__:
            if args.local_rank == 0:
                logging.info('Restoring NVIDIA AMP state from checkpoint')
            amp.load_state_dict(resume_state['amp'])
    del resume_state

    model_ema = None
    if args.model_ema:
        # Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
        model_ema = ModelEma(
            model,
            decay=args.model_ema_decay)
            #resume=args.resume)  # FIXME bit of a mess with bench
        if args.resume:
            load_checkpoint(unwrap_bench(model_ema), args.resume, use_ema=True)

    if args.distributed:
        if args.sync_bn:
            try:
                if has_apex:
                    model = convert_syncbn_model(model)
                else:
                    model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
                if args.local_rank == 0:
                    logging.info(
                        'Converted model to use Synchronized BatchNorm. WARNING: You may have issues if using '
                        'zero initialized BN layers (enabled by default for ResNets) while sync-bn enabled.')
            except Exception as e:
                logging.error('Failed to enable Synchronized BatchNorm. Install Apex or Torch >= 1.1')
        if has_apex:
            model = DDP(model, delay_allreduce=True)
        else:
            if args.local_rank == 0:
                logging.info("Using torch DistributedDataParallel. Install NVIDIA Apex for Apex DDP.")
            model = DDP(model, device_ids=[args.local_rank])  # can use device str in Torch >= 1.1
        # NOTE: EMA model does not need to be wrapped by DDP

    lr_scheduler, num_epochs = create_scheduler(args, optimizer)
    start_epoch = 0
    if args.start_epoch is not None:
        # a specified start_epoch will always override the resume epoch
        start_epoch = args.start_epoch
    elif resume_epoch is not None:
        start_epoch = resume_epoch
    if lr_scheduler is not None and start_epoch > 0:
        lr_scheduler.step(start_epoch)

    if args.local_rank == 0:
        logging.info('Scheduled epochs: {}'.format(num_epochs))

    train_anno_set = 'train2017'
    train_annotation_path = os.path.join(args.data, 'annotations', f'instances_{train_anno_set}.json')
    train_image_dir = train_anno_set
    dataset_train = CocoDetection(os.path.join(args.data, train_image_dir), train_annotation_path)

    # FIXME cutmix/mixup worth investigating?
    # collate_fn = None
    # if args.prefetcher and args.mixup > 0:
    #     collate_fn = FastCollateMixup(args.mixup, args.smoothing, args.num_classes)

    loader_train = create_loader(
        dataset_train,
        input_size=input_size,
        batch_size=args.batch_size,
        is_training=True,
        use_prefetcher=args.prefetcher,
        #re_prob=args.reprob,  # FIXME add back various augmentations
        #re_mode=args.remode,
        #re_count=args.recount,
        #re_split=args.resplit,
        #color_jitter=args.color_jitter,
        #auto_augment=args.aa,
        interpolation=args.train_interpolation,
        #mean=data_config['mean'],
        #std=data_config['std'],
        num_workers=args.workers,
        distributed=args.distributed,
        #collate_fn=collate_fn,
        pin_mem=args.pin_mem,
    )

    train_anno_set = 'val2017'
    train_annotation_path = os.path.join(args.data, 'annotations', f'instances_{train_anno_set}.json')
    train_image_dir = train_anno_set
    dataset_eval = CocoDetection(os.path.join(args.data, train_image_dir), train_annotation_path)

    loader_eval = create_loader(
        dataset_eval,
        input_size=input_size,
        batch_size=args.validation_batch_size_multiplier * args.batch_size,
        is_training=False,
        use_prefetcher=args.prefetcher,
        interpolation=args.interpolation,
        #mean=data_config['mean'],
        #std=data_config['std'],
        num_workers=args.workers,
        #distributed=args.distributed,
        pin_mem=args.pin_mem,
    )

    evaluator = COCOEvaluator(dataset_eval.coco, distributed=args.distributed)

    eval_metric = args.eval_metric
    best_metric = None
    best_epoch = None
    saver = None
    output_dir = ''
    if args.local_rank == 0:
        output_base = args.output if args.output else './output'
        exp_name = '-'.join([
            datetime.now().strftime("%Y%m%d-%H%M%S"),
            args.model
        ])
        output_dir = get_outdir(output_base, 'train', exp_name)
        decreasing = True if eval_metric == 'loss' else False
        saver = CheckpointSaver(checkpoint_dir=output_dir, decreasing=decreasing)
        with open(os.path.join(output_dir, 'args.yaml'), 'w') as f:
            f.write(args_text)

    try:
        for epoch in range(start_epoch, num_epochs):
            if args.distributed:
                loader_train.sampler.set_epoch(epoch)

            train_metrics = train_epoch(
                epoch, model, loader_train, optimizer, args,
                lr_scheduler=lr_scheduler, saver=saver, output_dir=output_dir, use_amp=use_amp, model_ema=model_ema)

            if args.distributed and args.dist_bn in ('broadcast', 'reduce'):
                if args.local_rank == 0:
                    logging.info("Distributing BatchNorm running means and vars")
                distribute_bn(model, args.world_size, args.dist_bn == 'reduce')

            # the overhead of evaluating with coco style datasets is fairly high, so just ema or non, not both
            if model_ema is not None:
                if args.distributed and args.dist_bn in ('broadcast', 'reduce'):
                    distribute_bn(model_ema, args.world_size, args.dist_bn == 'reduce')

                eval_metrics = validate(model_ema.ema, loader_eval, args, evaluator, log_suffix=' (EMA)')
            else:
                eval_metrics = validate(model, loader_eval, args, evaluator)

            if lr_scheduler is not None:
                # step LR for next epoch
                lr_scheduler.step(epoch + 1, eval_metrics[eval_metric])

            if saver is not None:
                update_summary(
                    epoch, train_metrics, eval_metrics, os.path.join(output_dir, 'summary.csv'),
                    write_header=best_metric is None)

                # save proper checkpoint with eval metric
                save_metric = eval_metrics[eval_metric]
                best_metric, best_epoch = saver.save_checkpoint(
                    unwrap_bench(model), optimizer, args,
                    epoch=epoch, model_ema=unwrap_bench(model_ema), metric=save_metric, use_amp=use_amp)

    except KeyboardInterrupt:
        pass
    if best_metric is not None:
        logging.info('*** Best metric: {0} (epoch {1})'.format(best_metric, best_epoch))


def train_epoch(
        epoch, model, loader, optimizer, args,
        lr_scheduler=None, saver=None, output_dir='', use_amp=False, model_ema=None):

    if args.prefetcher and args.mixup > 0 and loader.mixup_enabled:
        if args.mixup_off_epoch and epoch >= args.mixup_off_epoch:
            loader.mixup_enabled = False

    batch_time_m = AverageMeter()
    data_time_m = AverageMeter()
    losses_m = AverageMeter()

    model.train()

    end = time.time()
    last_idx = len(loader) - 1
    num_updates = epoch * len(loader)
    for batch_idx, (input, target) in enumerate(loader):
        last_batch = batch_idx == last_idx
        data_time_m.update(time.time() - end)

        output = model(input, target)
        loss = output['loss']

        if not args.distributed:
            losses_m.update(loss.item(), input.size(0))

        optimizer.zero_grad()
        if use_amp:
            with amp.scale_loss(loss, optimizer) as scaled_loss:
                scaled_loss.backward()
            if args.clip_grad:
                torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.clip_grad)
        else:
            loss.backward()
            if args.clip_grad:
                torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip_grad)
        optimizer.step()

        torch.cuda.synchronize()
        if model_ema is not None:
            model_ema.update(model)
        num_updates += 1

        batch_time_m.update(time.time() - end)
        if last_batch or batch_idx % args.log_interval == 0:
            lrl = [param_group['lr'] for param_group in optimizer.param_groups]
            lr = sum(lrl) / len(lrl)

            if args.distributed:
                reduced_loss = reduce_tensor(loss.data, args.world_size)
                losses_m.update(reduced_loss.item(), input.size(0))

            if args.local_rank == 0:
                logging.info(
                    'Train: {} [{:>4d}/{} ({:>3.0f}%)]  '
                    'Loss: {loss.val:>9.6f} ({loss.avg:>6.4f})  '
                    'Time: {batch_time.val:.3f}s, {rate:>7.2f}/s  '
                    '({batch_time.avg:.3f}s, {rate_avg:>7.2f}/s)  '
                    'LR: {lr:.3e}  '
                    'Data: {data_time.val:.3f} ({data_time.avg:.3f})'.format(
                        epoch,
                        batch_idx, len(loader),
                        100. * batch_idx / last_idx,
                        loss=losses_m,
                        batch_time=batch_time_m,
                        rate=input.size(0) * args.world_size / batch_time_m.val,
                        rate_avg=input.size(0) * args.world_size / batch_time_m.avg,
                        lr=lr,
                        data_time=data_time_m))

                if args.save_images and output_dir:
                    torchvision.utils.save_image(
                        input,
                        os.path.join(output_dir, 'train-batch-%d.jpg' % batch_idx),
                        padding=0,
                        normalize=True)

        if saver is not None and args.recovery_interval and (
                last_batch or (batch_idx + 1) % args.recovery_interval == 0):
            saver.save_recovery(
                unwrap_bench(model), optimizer, args, epoch, model_ema=unwrap_bench(model_ema),
                use_amp=use_amp, batch_idx=batch_idx)

        if lr_scheduler is not None:
            lr_scheduler.step_update(num_updates=num_updates, metric=losses_m.avg)

        end = time.time()
        # end for

    if hasattr(optimizer, 'sync_lookahead'):
        optimizer.sync_lookahead()

    return OrderedDict([('loss', losses_m.avg)])


def validate(model, loader, args, evaluator=None, log_suffix=''):
    batch_time_m = AverageMeter()
    losses_m = AverageMeter()

    model.eval()

    end = time.time()
    last_idx = len(loader) - 1
    with torch.no_grad():
        for batch_idx, (input, target) in enumerate(loader):
            last_batch = batch_idx == last_idx

            output = model(input, target)
            loss = output['loss']

            if evaluator is not None:
                evaluator.add_predictions(output['detections'], target)

            if args.distributed:
                reduced_loss = reduce_tensor(loss.data, args.world_size)
            else:
                reduced_loss = loss.data

            torch.cuda.synchronize()

            losses_m.update(reduced_loss.item(), input.size(0))

            batch_time_m.update(time.time() - end)
            end = time.time()
            if args.local_rank == 0 and (last_batch or batch_idx % args.log_interval == 0):
                log_name = 'Test' + log_suffix
                logging.info(
                    '{0}: [{1:>4d}/{2}]  '
                    'Time: {batch_time.val:.3f} ({batch_time.avg:.3f})  '
                    'Loss: {loss.val:>7.4f} ({loss.avg:>6.4f})  '.format(
                        log_name, batch_idx, last_idx, batch_time=batch_time_m, loss=losses_m))

    metrics = OrderedDict([('loss', losses_m.avg)])
    if evaluator is not None:
        metrics['map'] = evaluator.evaluate()

    return metrics


if __name__ == '__main__':
    main()